Journal
BIOINFORMATICS
Volume 33, Issue 14, Pages I152-I160Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btx270
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Funding
- US National Science Foundation (NSF) CAREER Award [CCF-1053753]
- US National Institutes of Health (NIH) [R01HG005690, R01HG007069, R01CA180776]
- Career Award at the Scientific Interface from the Burroughs Wellcome Fund
- Alfred P. Sloan Research Fellowship
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1247581] Funding Source: National Science Foundation
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Motivation: A tumor arises from an evolutionary process that can be modeled as a phylogenetic tree. However, reconstructing this tree is challenging as most cancer sequencing uses bulk tumor tissue containing heterogeneous mixtures of cells. Results: We introduce Probabilistic Algorithm for Somatic Tree Inference (PASTRI), a new algorithm for bulk-tumor sequencing data that clusters somatic mutations into clones and infers a phylogenetic tree that describes the evolutionary history of the tumor. PASTRI uses an importance sampling algorithm that combines a probabilistic model of DNA sequencing data with a enumeration algorithm based on the combinatorial constraints defined by the underlying phylogenetic tree. As a result, tree inference is fast, accurate and robust to noise. We demonstrate on simulated data that PASTRI outperforms other cancer phylogeny algorithms in terms of runtime and accuracy. On real data from a chronic lymphocytic leukemia (CLL) patient, we show that a simple linear phylogeny better explains the data the complex branching phylogeny that was previously reported. PASTRI provides a robust approach for phylogenetic tree inference from mixed samples.
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